Bioinformatics 3 V 2 – Clusters, Dijkstra, and Graph Layout

Bioinformatics 3V 2 – Clusters, Dijkstra, and Graph Layout Fri, Oct 19, 2012

Graph Basics A graphG is an ordered pair (V, E) of a set V of vertices and a set E of edges. Degree distributionP(k) Random network: also called the "Erdös-Renyi model" start from all nodes, add links randomly P(k) = "Poisson" Scale-free network: grow with preferential attachment P(k) = power law

Connected Components Connected graph <=> there is a path between all pairs of nodes In large (random) networks: complete {V} often not connected  identify connected subsets {Vi} with {V} = U {Vi}  connected components (CC) #CC = 5 Nmax = 15 Nmin = 1

Connectivity of the Neighborhood How many of the neighboring vertices are themselves neighbors? => clustering coefficient C(k) Number of possible edges between k nodes: nk is the actual number of edges between the neighbor nodes. Fraction of actual edges clustering coefficient green: k = 2, nk= 1 C = 1 red: k = 4, nk = 2 C = 1/3 blue: k = 1, nk = ? C is not defined Note: clustering coeff. sometimes also defined via fraction of possible triangles

Cluster Coefficient of a Graph Data: Cifor each node i N values Statistics: average at fixed k  Ci, C(k), <C> average over all nodes  k • Note: it is also possible to average the C(k) • different value for <C> !!! • Because no weighting for • different occupancy of k’s.

C(k) for a Random Network Cluster coefficient when m edges exist between k neighbors Probability to have exactly m edges between the k neighbors Average C(k) for degree k: C(k) is independent of k <=> same local connectivity throughout the network

The Percolation Threshold Connected component = all vertices that are connected by a path Percolation transition at λ = 2 • Very few edges • only CCs • of size 2 Lots of edges  graph is one CC Identify: Ncc = number of connected components (clusters) Nmax = size of the largest cluster For λ > 2: giant component exists average degree λ

Percolation Transition Example: regular square lattice, N = 25 nodes, Lmax = 40 links between next neighbors L = 3 λ = 0.24 Ncc = 22 Nmax = 2 L = 11 λ = 0.88 Ncc = 14 Nmax = 4 L = 22 λ = 1.76 Ncc = 3 Nmax = 15 L = 24 λ = 1.92 Ncc = 1 Nmax= 25 "percolation" percolation = "spanning structure" emerges (long range connectivity) for an infinite square lattice: percolation transition at λ = 2 here: finite size effect <=> fewer possible links at the boundaries

Clusters in scale free graphs Scale-free network <=> no intrinsic scale  same properties at any k-level  same local connectivity C(k) = const. "Real" biological data  missing links  multiple clusters Ci, C(k), <C> Is the metabolic network of a cell fully connected? k

Algorithms on Graphs How to represent a graph in the computer? 1. Adjacency list => list of neighbors for each node 1: 2: 3: 4: 5: 6: 7: (3) (3) (1, 2, 4, 5) (3, 5, 6) (3, 4, 6, 7) (4, 5) (5) + minimal memory requirement + vertices can easily be added or removed – requires O(λ) time to determine whether a certain edge exists Note: for weighted graphs store pairs of (neighbor label, edge weight)

Graph Representation II 2. Adjacency matrix N x N matrix with entries Muv Muv = weight when edge between u and v exists, 0 otherwise  symmetric for undirected graphs + fast O(1) lookup of edges – large memory requirements – adding or removing nodes is expensive Note: very convenient in programming languages that support sparse multi-dimensional arrays => Perl

Graph Representation III 3. Incidence matrix N x M matrix with entries Mnm Mnm = weight when edge m ends at node n 0 otherwise e5 e6 e1 e3 e7 e4 e2  for a plain graph there are two entries per column  directed graph: indicate direction via sign (in/out) The incidence matrix is a special form of the stoichiometric matrix of reaction networks.

The Shortest Path Problem Problem: Find the shortest path from a given vertex to the other vertices of the graph (Dijkstra 1959). We need (input): • weighted graph G(V, E) • start (source) vertex s in G Edsger Dijkstra (1930-2002): We get (output): • shortest distances d[v] between s and v • shortest paths from s to v Idea: Always proceed with the closest node  greedy algorithm Real world application:  GPS navigation devices

Dijkstra Algorithm 0 Initialization: for all nodes v in G: d[v] = oo pred[v] = nil d[s] = 0 distance and path to all other nodes is still unknown distance from source to source = 0 d[v] = length of path from s to v pred[v] = predecessor node on the shortest path In the example: s = 1

Dijkstra I Iteration: Q = V while Q is not empty: u = node with minimal d if d[u] = oo: break delete u from Q for each neighbor v of u: d_temp = d[u] + d(u,v) if d_temp < d[v]: d[v] = d_temp pred[v] = u return pred[]C Save {V} into working copy Q choose node closest to s exit if all remaining nodes are inaccessible calculate distance to u's neighbors if new path is shorter => update

Q = (2, 3, 4, 5, 6, 7) Q = (2, 3, 5, 6, 7) Q = (2, 5, 6, 7) 2) 4) 3) Dijkstra-Example 1) Q = (1, 2, 3, 4, 5, 6, 7) Q = V while Q is not empty: u = node with minimal d if d[u] = oo: break delete u from Q for each neighbor v of u: d_temp = d[u] + d(u,v) if d_temp < d[v]: d[v] = d_temp pred[v] = u return pred[]C

Final result: 5) d(1, 6) = 37 path = (1, 4, 6) or (1,4,5,6) Q = (5, 6, 7) d(1, 7) = 42 path = (1, 4, 3, 2, 7) Example contd. Q = (2, 5, 6, 7) 4) Q = (6, 7) Q = (7)

Dijkstra works for directed and undirected graphs with non-negative weights. Straight-forward implementation: O(N2) Beyond Dijkstra Graphs with positive and negative weights Bellman-Ford-algorithm If there is a heuristic to estimate weights:  improve efficiency of Dijkstra A*-algorithm

Graph Layout Task: visualize various interaction data: e.g. protein interaction data (undirected): nodes – proteins edges – interactions metabolic pathways (directed) nodes – substances edges – reactions regulatory networks (directed): nodes – transcription factors + regulated proteins edges – regulatory interaction co-localization (undirected) nodes – proteins edges – co-localization information homology (undirected/directed) nodes – proteins edges – sequence similarity (BLAST score)

Graph Layout Algorithms Graphs encapsulate relationship between objects  drawing gives visual impression of these relations Good Graph Layout: aesthetic • minimal edge crossing • highlight symmetry (when present in the data) • even spacing between the nodes Many approaches in literature (and in software tools), most useful ones usually NP-complete (exponential runtime) Most popular for straight-edge-drawing: force-directed: spring model or spring-electrical model embedding algorithms like H3 or LGL

Force-Directed Layout • Peter Eades (1984): graph layout heuristic • "Spring Embedder'' algorithm. • • edges  springs • vertices  rings that connect the springs • • Layout by dynamic relaxation •  lowest-energy conformation •  "Force Directed'' algorithm http://www.hpc.unm.edu/~sunls/research/treelayout/node1.html

Energy and Force Energy: describes the altitude of the landscape Energy increases when you go up the hill You need more force for a steeper ascent Force: describes the change of the altitude, points downwards.

Spring Embedder Layout Springs regulate the mutual distance between the nodes • too close  repulsive force • too far  attractive force Spring embedder algorithm: • add springs for all edges • add loose springs to all non-adjacent vertex pairs Total energy of the system: xi, xj= position vectors for nodes i and j lij = rest length of the spring between i and j R = spring constant (stiffness) Problem: lijhave to be determined a priori, e.g., from network distance

Spring Model Layout Task: find configuration of minimal energy In 2D/3D: force = negative gradient of the energy  Iteratively move nodes "downhill" along the gradient of the energy  displace nodes proportional to the force acting on them Problems: • local minima • a priori knowledge of all spring lengths  works best for regular grids

The Spring-Electrical-Model More general model than spring embedder model: use two types of forces 1) attractive harmonic force between connected nodes (springs) one uses usually the same spring constant k for all edges 2) repulsiveCoulomb-like force between all nodes "all nodes have like charges"  repulsion either Qij = Q or, e.g., Qij = ki kj Repulsion pulls all nodes apart, springs keep connected nodes together workhorse method for small to medium sized graphs  Do-it-yourself in Assignment 2 <=

Spring-Electrical Example http://www.it.usyd.edu.au/~aquigley/3dfade/

Force-Directed Layout: Summary Analogy to a physical system => force directed layout methods tend to meet various aesthetic standards: • efficient space filling, • uniform edge length (with equal weights and repulsions) • symmetry • smooth animation of the layout process (visual continuity) Force directed graph layout  the "work horse" of layout algorithms. Not so nice: the initial random placement of nodes and even very small changes of layout parameters will lead to different representations. (no unique solution) Side-effect: vertices at the periphery tend to be closer to each other than those in the center…

Runtime Scaling Force directed layout: Several possible arrangements!!! (local minima) loop until convergence: calculate forces: L springs N(N-1)/2 charge pairs move vertices output positions O(N2)!!!  force directed layout suitable for small to medium graphs (≤ O(1000) nodes?) Speed up layout by: • multi-level techniques to overcome local minima • clustering (octree) methods for distant groups of nodes O(N log N)

H3 Algorithm Two problems of force directed layout: • runtime scaling • 2D space for drawing the graph Tamara Munzner (1996-1998): H3 algorithm  interactively visualize large datasets of ∼100.000 nodes. • focusses on quasi-hierarchical graphs  use a spanning tree as the backbone of a layout algorithm • graph layout in exponential space (projected on 2D for interactive viewing) Spanning tree: connected acyclic subgraph that contains all the vertices of the original graph, but does not have to include all the links  find a minimum-weight spanning tree through a graph with weighted edges, where domain-specific information is used to compute the weights

Spanning Tree Some algorithms work only/better on trees Idea: remove links until graph has tree structure, keep all nodes connected  spanning tree Minimal spanning tree = spanning tree with the least total weight of the edges Greedy Kruskal-Algorithm: iteratively choose unused edge with least weight, if it does not lead to a circle! greedy <=> base choice on current state, (locally optimal choice)

Proof that there is no spanning tree with a lower weight? Kruskal - Example Minimum spanning tree weight = 66

Spanning Tree for a web site Constructing a spanning tree for a quasi-hierarchical web site. Top Left: The hyperlink structure of a simple hypothetical site, as it would be reported by a web spider starting at the top page. Nodes represent web pages, and links represent hyperlinks. Although the graph structure itself is determined by hyperlinks, additional information about hierarchical directory structure of the site’s files is encoded in the URLs. Top Row: We build up the graph incrementally, one link at a time. Middle Row: We continue adding nodes without moving any of the old ones around. Bottom Row: When the animal/wombat.html page is added, the label matching test shows that animal is a more appropriate parent than /TOC.html, so the node moves and the link between animal/wombat.html and /TOC.html becomes a non-tree link. In the final stage, note that bird/emu.html does not move when the bird is added, even though the labels match, because there is no hyperlink between them. PhD thesis Tamara Munzner, chapter 3

Cone Layout Place the nodes according to their hierarchy starting from the root node  direction indicates lineage For arbitrary graphs  how to get weights?  which node is the root?

Exponential Room In Euklidian space: circumference of a circle grows linear: U = 2πr In hyperbolic space: U = 2π sinh r  exponentially growing space on the circle For (cone) graph layout  there is enough room for yet another level Also: mappings of the complete hyperbolic space  finite volume of Euklidian space PhD thesis Tamara Munzner, chapter 3

Models of hyperbolic space PhD thesis Tamara Munzner, chapter 3

Visualization with H3 PhD thesis Tamara Munzner, chapter 3

GIFs don't work here… http://www.caida.org/tools/visualization/walrus/gallery1/ H3: + layout based on MST  fast + layout in hyperbolic space  enough room – how to get the MST for biological graphs????

Summary What you learned today:  Local connectivity: clustering  shortest path: Dijkstra algorithm  graph layout: force-directed and embedding schemes  spanning tree: Kruskal algorithm Next lecture:  biological data to build networks from

Bioinformatics 3 V 2 – Clusters, Dijkstra, and Graph Layout